Feature Screening for High-Dimensional Survival Data via Censored Quantile Correlation

This paper proposes a new sure independence screening procedure for high-dimensional survival data based on censored quantile correlation (CQC). This framework has two distinctive features: 1) Via incorporating a weighting scheme, our metric is a natural extension of quantile correlation (QC), considered by Li (2015), to handle high-dimensional survival data; 2) The proposed method not only is robust against outliers, but also can discover the nonlinear relationship between independent variables and censored dependent variable. Additionally, the proposed method enjoys the sure screening property under certain technical conditions. Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors.

     

Single-Index Quantile Regression with Left Truncated Data

The purpose of this paper is two fold. First, the authors investigate quantile regression(QR) estimation for single-index QR models when the response is subject to random left truncation.The random weights are introduced to deal with left truncated data and the associated iteration estimation method is proposed. The asymptotic properties for the proposed QR estimates of the index parameter and unknown link function are both obtained. Further, by combining the QR loss function and the adaptive LA...     

Testing High-Dimensional Nonparametric Behrens-Fisher Problem

Journal of Systems Science and Complexity - For high-dimensional nonparametric Behrens-Fisher problem in which the data dimension is larger than the sample size, the authors propose two test...     

基于LWSVR的繁忙机场航班滑出时间预测

针对繁忙机场航班滑出时间预测准确率低的问题,结合局部回归和加权支持向量回归,提出基于局部加权支持向量回归的离港航班滑出时间预测模型。该模型采用K最近邻方法,减小训练样本集容量,并为每个预测样本构建一个预测模型。通过计算训练样本与预测样本间的马氏距离,来优化加权支持向量回归中高斯核加权函数的带宽参数,获得加权系数。结合某机场离港航班数据仿真分析,实验结果表明模型在误差允许范围内的预测准确率达到83.33%,模型更加稳定。     

Tensor Decomposition and High-Performance Computing for Solving High-Dimensional Stochastic Control System Numerically

The paper presents a numerical method for solving a class of high-dimensional stochastic control systems based on tensor decomposition and parallel computing. The HJB solution provides a globally optimal controller to the associated dynamical system. Variable substitution is used to simplify the nonlinear HJB equation. The curse of dimensionality is avoided by representing the HJB equation using separated representation. Alternating least squares (ALS) is used to reduced the separation rank. The experiment is conducted and the numerical solution is obtained. A high-performance algorithm is designed to reduce the separation rank in the parallel environment, solving the high-dimensional HJB equation with high efficiency.

     

关于智能故障诊断的研究

把模糊技术与判别分析法结合起来 ,对于故障诊断样本 ,建立广义诊断矩阵 ,给出了关于智能故障诊断的新方法。该方法是选取多元统计分析中的马氏距离 ,依据概率分布的原则对故障征兆进行判别 ,并通过仿真模拟 ,显示其可行性及有效性     

集成多普勒观测的目标机动检测算法

基于决策的单模目标跟踪方法的关键是及时而稳健的目标机动检测,充分利用目标多普勒观测量能够有效提高机动检测性能。提出一种集成多普勒观测的目标机动检测算法,利用基于马氏距离的预测寻优方法,克服了多普勒观测噪声水平较高时估计式无解的情况,提高了加速度估计精度;基于奈曼皮尔逊准则设计机动检测器,避免了因目标机动检测的滞后性带来的门限漂移。仿真实验表明,算法提高了加速度估计的精度和稳健性,显著降低了平均检测延迟,有效提高了机动检测性能。     

基于节点信任值的WSNs入侵检测方案

为了保证无线传感器网络(wireless sensor network,WSNs)内部节点入侵检测中具有较高的检测率和较低的误检率,提出了一种基于节点信任值的层簇式WSNs入侵检测方案。该方案通过分析WSNs中典型网络攻击特征,定义了节点的多种典型信任属性,并利用马氏距离判断节点信任属性是否异常来获知节点是否存在异常,最后利用贝塔分布理论和异常折扣因子相结合实现节点信任值的计算和更新,从而实现节点入侵检测判断。经仿真,结果表明该方案可实现常见入侵的检测,具有较高的检测率和较低的误检率。     

Test on Stochastic Block Model: Local Smoothing and Extreme Value Theory

In this paper, to obtain a consistent estimator of the number of communities, the authors present a new sequential testing procedure, based on the locally smoothed adjacency matrix and the extreme value theory. Under the null hypothesis, the test statistic converges to the type I extreme value distribution, and otherwise, it explodes fast and the divergence rate could even reach n in the strong signal case where n is the size of the network, guaranteeing high detection power. This method is simple to use and serves as an alternative approach to the novel one in Lei (2016) using random matrix theory. To detect the change of the community structure, the authors also propose a two-sample test for the stochastic block model with two observed adjacency matrices. Simulation studies justify the theory. The authors apply the proposed method to the political blog data set and find reasonable group structures.

     

Ridge-forward quadratic discriminant analysis in high-dimensional situations

Quadratic discriminant analysis is a classical and popular classification tool, but it fails to work in high-dimensional situations where the dimension p is larger than the sample size n. To address this issue, the authors propose a ridge-forward quadratic discriminant (RFQD) analysis method via screening relevant predictors in a successive manner to reduce misclassification rate. The authors use extended Bayesian information criterion to determine the final model and prove that RFQD is selection consistent. Monte Carlo simulations are conducted to examine its performance.     

面向入侵检测系统的Deep Belief Nets模型

连续的网络流量会导致海量数据问题,这为入侵检测提出了新的挑战。为此,提出一种面向入侵检测系统的深度信念网络(deep belief nets oriented to the intrusion detection system, DBN-IDS）模型。首先,通过无监督的、贪婪的算法自底向上逐层训练每一个受限玻尔兹曼机(restricted Boltzmann machine,RBM)网络,使得大量高维、非线性的无标签数据映射为最优的低维表示；然后利用带标签数据被附加到顶层,通过反向传播(back-propagation,BP）算法自顶向下有监督地对RBM网络输出的低维表示进行分类,并同时对RBM网络进行微调；最后,利用NSL-KDD数据集对模型参数和性能进行了深入的分析。实验结果表明,DBN-IDS分类效果优于支持向量机(support vector machine,SVM)和神经网络(neural network，NN),适用于高维、非线性的海量入侵数据的分类处理。     

改进的多目标灰靶决策方法

考虑到各指标间的相关性、不同量纲和重要性差异对决策效果的影响,利用加权马氏距离对传统的灰靶决策方法进行改进,并研究了改进后灰靶的性质。结果表明,改进后的灰靶满足：靶心距对效果样本作非奇异线性变换的不变性;当观测指标间不存在相关性时,加权马氏距离就是加权欧氏距离。避免了决策指标间的相关性、不同量纲和重要性差异对决策效果的影响以及灰靶变换的不相容问题。最后以实例验证了该模型的有效性与实用性。     

通信辐射源运动轨迹相似性分析

针对传统运动轨迹相似性分析利用时空信息效率不高问题,结合通信辐射源主体特点,提出基于时空和语义信息的辐射源轨迹相似性分析算法。在该算法中,对相似运动轨迹距离测度进行改进,提出基于马氏距离的轨迹时空相似度算法,将轨迹分割后计算相似度,克服了时间不匹配轨迹和不同采样间隔对轨迹相似性判断的影响;同时,结合基于高效K近邻(K-nearest neighbor, KNN)相似搜索的语义相似数据搜索算法,实现对辐射源相似轨迹的准确判断。该算法能够准确判断相似轨迹,优于现有的相似轨迹判断方法,且适应噪声环境。仿真实验结果验证了该算法的性能。     

通信辐射源运动轨迹相似性分析

针对传统运动轨迹相似性分析利用时空信息效率不高问题,结合通信辐射源主体特点,提出基于时空和语义信息的辐射源轨迹相似性分析算法。在该算法中,对相似运动轨迹距离测度进行改进,提出基于马氏距离的轨迹时空相似度算法,将轨迹分割后计算相似度,克服了时间不匹配轨迹和不同采样间隔对轨迹相似性判断的影响;同时,结合基于高效K近邻(K-nearest neighbor, KNN)相似搜索的语义相似数据搜索算法,实现对辐射源相似轨迹的准确判断。该算法能够准确判断相似轨迹,优于现有的相似轨迹判断方法,且适应噪声环境。仿真实验结果验证了该算法的性能。     

Comparison of Covariate Balance Weighting Methods in Estimating Treatment Effects

Different covariate balance weighting methods have been proposed by researchers from different perspectives to estimate the treatment effects. This paper gives a brief review of the covariate balancing propensity score method by Imai and Ratkovic (2014), the stable balance weighting procedure by Zubizarreta (2015), the calibration balance weighting approach by Chan, et al. (2016), and the integrated propensity score technique by Sant’Anna, et al. (2020). Simulations are conducted to illustrate the finite sample performance of both the average treatment effect and quantile treatment effect estimators based on different weighting methods. Simulation results show that in general, the covariate balance weighting methods can outperform the conventional maximum likelihood estimation method while the performance of the four covariate balance weighting methods varies with the data generating processes. Finally, the four covariate balance weighting methods are applied to estimate the treatment effects of the college graduate on personal annual income.

     

基于AR模型的飞机尾流距离检测方法

尾流复杂的电磁散射特性使得传统基于快速傅里叶变换(fast Fourier transformation, FFT)的频域检测方法不适用。首先,基于FFT的谱估计方法频率分辨率较低,尤其在短脉冲序列条件下性能较差;其次,尾流作为一种频域扩展目标,其回波能量在频域较为分散,单通道或多通道求和检测都会损失较多能量。利用分辨率更高的自回归(autoregressive, AR) 模型进行谱估计,并基于AR流形上的距离定义一种距离检测器,将尾流谱整体进行检测,能充分利用尾流的多普勒信息,通过利用尾流数据对距离检测器和传统方法的检测性能进行仿真比较分析,验证了所提方法的有效性。     

基于黄金分割法的加速MARS研究

多元自适应回归样条法(Multivariate adaptive regression spline,MARS)是一种专门针对高维数据拟合的回归方法。该方法以样条函数的张量积作为基函数,以样本数据坐标作为可选的节点矢量值,算法过程以失拟度(Lack of fit,LOF)最小为目标优化选择基函数和节点矢量。提出了基于黄金分割的加速MARS算法,引入一维黄金分割搜索算法及计算子区间概念,以提高算法节点矢量和基函数的优化选择效率。最后,四个典型的测试算例验证了所提出的方法在保证模型近似精度的前提下可大幅节省模型构造时间。     

二维最大散度差图像投影鉴别分析

提出了一种新的二维散度差图像投影鉴别分析方法。该方法利用类间离散度与类内离散度之差作为鉴别准则，从根本上避免了传统的Fisher线性鉴别分析所遇到的小样本问题时。所提出的方法是直接基于图像矩阵的，与以往的基于图像向量的鉴别方法相比，它的突出优点是大大提高了特征抽取的速度。在ORL人脸数据库和AR标准人脸库上的仿真试验结果表明，所提出的方法不仅在识别性能上优于传统的散度差鉴别分析，特征抽取的速度有了较大幅度的提高。     

基于样本重采样的电路非平衡数据预处理方法

针对机载设备电子电路故障状态测试数据少、整体测试数据不均衡的问题,提出了一种基于样本重采样的数据预处理方法。首先,采用超限学习机对原始数据集进行训练以挑选出分类准确的样本。然后,对其中的少数类和多数类分别采用合成少数类过采样技术(synthetic minority oversampling technique, SMOTE)进行过采样和局部密度欠采样处理;并将错误分类的多数类样本作为干扰因素进行删除。通过以上两种手段可以均衡数据集,并控制数据规模防止过拟合,提高对故障样本的检测率。实测数据处理结果表明,相比于其他重采样算法,所提算法整体效果优良且稳定,对电子电路故障诊断具有一定的应用价值。     

基于样本重采样的电路非平衡数据预处理方法

针对机载设备电子电路故障状态测试数据少、整体测试数据不均衡的问题,提出了一种基于样本重采样的数据预处理方法。首先，采用超限学习机对原始数据集进行训练以挑选出分类准确的样本。然后，对其中的少数类和多数类分别采用合成少数类过采样技术(synthetic minority oversampling technique, SMOTE)进行过采样和局部密度欠采样处理;并将错误分类的多数类样本作为干扰因素进行删除。通过以上两种手段可以均衡数据集,并控制数据规模防止过拟合,提高对故障样本的检测率。实测数据处理结果表明,相比于其他重采样算法,所提算法整体效果优良且稳定,对电子电路故障诊断具有一定的应用价值。